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居家跌倒风险评估与检测传感器系统

In-home fall risk assessment and detection sensor system.

作者信息

Rantz Marilyn J, Skubic Marjorie, Abbott Carmen, Galambos Colleen, Pak Youngju, Ho Dominic K C, Stone Erik E, Rui Liyang, Back Jessica, Miller Steven J

机构信息

Sinclair School of Nursing, Family and Community Medicine, University of Missouri, Columbia, MO, USA.

出版信息

J Gerontol Nurs. 2013 Jul;39(7):18-22. doi: 10.3928/00989134-20130503-01. Epub 2013 May 15.

DOI:10.3928/00989134-20130503-01
PMID:23675644
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC3817841/
Abstract

Falls are a major problem in older adults. A continuous, unobtrusive, environmentally mounted (i.e., embedded into the environment and not worn by the individual), in-home monitoring system that automatically detects when falls have occurred or when the risk of falling is increasing could alert health care providers and family members to intervene to improve physical function or manage illnesses that may precipitate falls. Researchers at the University of Missouri Center for Eldercare and Rehabilitation Technology are testing such sensor systems for fall risk assessment (FRA) and detection in older adults' apartments in a senior living community. Initial results comparing ground truth (validated measures) of FRA data and GAITRite System parameters with data captured from Microsoft(®) Kinect and pulse-Doppler radar are reported.

摘要

跌倒在老年人中是一个重大问题。一种连续、不引人注意、安装在环境中的(即嵌入到环境中而非个人佩戴的)家庭监测系统,能够自动检测跌倒何时发生或跌倒风险何时增加,这可以提醒医疗保健提供者和家庭成员进行干预,以改善身体功能或管理可能引发跌倒的疾病。密苏里大学老年护理与康复技术中心的研究人员正在一个老年生活社区的老年人公寓中测试这种传感器系统,用于跌倒风险评估(FRA)和检测。本文报告了将FRA数据和GAITRite系统参数的地面真值(经过验证的测量值)与从微软(®)Kinect和脉冲多普勒雷达捕获的数据进行比较的初步结果。

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